• Naveen kumar G
• Sai Nikitha C

This paper displays another technique for conclusion investigation in Facebook that, beginning from messages composed by clients, underpins: (I) to extricate data about the clients' slant extremity (positive, nonpartisan or negative), as transmitted in the messages they compose; and (ii) to show the clients' typical supposition extremity and to distinguish critical passionate changes. We have actualized this strategy in SentBuk, a Facebook application additionally exhibited in this paper. SentBuk recovers messages composed by clients in Facebook and orders them as per their extremity, demonstrating the outcomes to the clients through an intuitive interface. It likewise underpins enthusiastic change location, companion's feeling discovering, client characterization as per their messages, and insights, among others. The arrangement strategy executed in SentBuk pursues a half and half approach: it joins lexical-based and AI procedures. The outcomes got through this methodology demonstrate that it is plausible to perform notion examination in Facebook with high exactness (83.27%). With regards to e-learning, it is extremely helpful to have data about the clients' slants accessible. On one hand, this data can be utilized by versatile e-learning frameworks to help customized learning, by considering the client's passionate state when suggesting him/her the most appropriate exercises to be handled at each time. Then again, the understudies' suppositions towards a course can fill in as input for educators, particularly on account of web based realizing, where up close and personal contact is less continuous. The value of this work with regards to e-learning, both for instructors and for versatile frameworks, is depicted as well.

Sentiment, Facebook, N-gram, Lexicon-approach

"PRODUCT REVIEW USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.730-735, June 2019, Available :http://www.jetir.org/papers/JETIR1907M97.pdf

"PRODUCT REVIEW USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp730-735, June 2019, Available at : http://www.jetir.org/papers/JETIR1907M97.pdf